mlsanityData to 7 Oct 2026

Depth Super-Resolution from Explicit and Implicit High-Frequency Features

arXiv 2303.09307 ↗First posted 16 Mar 2023cs.CVcited by 5

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Abstract (arXiv)

We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The former are extracted by an efficient transformer processing both local and global contexts, while the latter are obtained by projecting color images into the frequency domain. Both are combined together with depth features by means of a fusion strategy within a multi-stage and multi-scale framework. Experiments on the main benchmarks, such as NYUv2, Middlebury, DIML and RGBDD, show that our approach outperforms existing methods by a large margin (~20% on NYUv2 and DIML against the contemporary work DADA, with 16x upsampling), establishing a new state-of-the-art in the guided depth super-resolution task.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 41 of its 82 references that are in the map. First posted on arXiv 16 Mar 2023.

Builds on

41 of 82 references.

References · 41

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29 more
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Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2023SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolutioncited by 27
  2. 2024RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methodscited by 8
  3. 2024Cross-spectral Gated-RGB Stereo Depth Estimationcited by 8
  4. 2025Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyondcited by 1
  5. 2024Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth Estimationcited by 0